Document Classification Using Layout Analysis

This paper describes methods for documents image classification at the spatial layout level. The goal is to develop fast algorithms for initial document type classification without OCR, which can then be verified using more elaborate methods based on more detailed geometric and syntactic models. This is based on the assumption that different types of printed documents often have fairly distinct spatial layout styles. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. We demonstrate the usefulness of these features derived from interval coding, in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in this paper can be used in various document processing tasks including document retrieval, understanding and routing.